An Early Degradation Detection Method for Output Filter Stage of a DC/DC Buck Converter

As critical components in power electronic systems for energy conversion and power management, dc/dc converters are commonly used in aerospace, electric vehicles, and other electronic devices. Their degradation may lead to instability failure of the power electronic system, significantly impacting the reliability of devices. To prevent irreversible damage, various degradation detection methods have been proposed, which have advantages such as automatic, real-time, accurate, and noninvasive. However, few studies have considered effective degradation detection with reduced resource demands and sampling costs. In this article, a signal reconstruction technique is adopted in signal acquisition and a novel early degradation detection method is proposed for the output filter capacitor and energy storage inductor of a dc/dc buck converter. In the proposed method, degradation features are extracted from output voltage ripples through long short-term memory (LSTM) fully convolutional network and contrastive learning. Based on the learned features of normal and degraded samples, the converter degradation state is identified. To enhance robustness against noise, the Mixup-based data augmentation is applied to increase data diversity. The effectiveness of the proposed method is verified by simulation and experiment.